Sound Events and Emotions: Investigating the
Relation of Rhythmic Characteristics and Arousal
K. Drossos 1 R. Kotsakis 2 G...
Agenda
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s La...
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Everyday life
Everyday life
St...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
Music and e...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound and emotions
From music ...
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Objectives of current study
Da...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Objectives of current study
Da...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Objectives of current study
Da...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Objectives of current study
Da...
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Experimental Procedure’s Layou...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Sound corpus & emotional model...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Pre-proce...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Feature E...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Feature E...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Feature E...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Feature E...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Processing of Sounds
Feature E...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Feature...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Feature...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Feature...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Classifi...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Classifi...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Machine learning tests
Classifi...
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Feature Eva...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Feature Eva...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Feature Eva...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Feature Eva...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Feature Eva...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification
Classification
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification
Classification
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Classification
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification
Classification
Ge...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification
Classification
Ge...
1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
Features
Mo...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature evaluation
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification results
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Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Classification results
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1. Introduction
Everyday life
Sound and emotions
2. Objectives
3. Experimental Sequence
Experimental Procedure’s Layout
So...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature work
Features & Dimens...
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work
Feature work
Features & Dimens...
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Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal

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A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm- related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and non- musical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener’s triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

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Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal

  1. 1. Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal K. Drossos 1 R. Kotsakis 2 G. Kalliris 2 A. Floros 1 1Digital Audio Processing & Applications Group, Audiovisual Signal Processing Laboratory, Dept. of Audiovisual Arts, Ionian University, Corfu, Greece 2Laboratory of Electronic Media, Dept. of Journalism and Mass Communication, Aristotle University of Thessaloniki, Thessaloniki, Greece
  2. 2. Agenda 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  3. 3. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  4. 4. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  5. 5. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  6. 6. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  7. 7. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  8. 8. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  9. 9. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
  10. 10. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
  11. 11. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  12. 12. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  13. 13. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  14. 14. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  15. 15. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  16. 16. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  17. 17. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  18. 18. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  19. 19. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  20. 20. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  21. 21. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  22. 22. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  23. 23. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  24. 24. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  25. 25. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  26. 26. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  27. 27. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  28. 28. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  29. 29. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  30. 30. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  31. 31. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  32. 32. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  33. 33. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  34. 34. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  35. 35. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  36. 36. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  37. 37. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  38. 38. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  39. 39. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  40. 40. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  41. 41. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  42. 42. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  43. 43. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  44. 44. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  45. 45. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  46. 46. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  47. 47. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  48. 48. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  49. 49. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  50. 50. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  51. 51. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  52. 52. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  53. 53. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  54. 54. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  55. 55. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  56. 56. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  57. 57. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  58. 58. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  59. 59. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  60. 60. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  61. 61. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  62. 62. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  63. 63. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Extracted Features Statistical Measures Beat spectrum Mean Onsets Standard deviation Tempo Gradient Fluctuation Kurtosis Event density Skewness Pulse clarity
  64. 64. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  65. 65. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  66. 66. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  67. 67. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  68. 68. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  69. 69. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  70. 70. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  71. 71. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  72. 72. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  73. 73. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  74. 74. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  75. 75. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation Upper group Beatspectrum std Event density std Onsets gradient Fluctuation kurtosis Beatspectrum gradient Pulse clarity std Fluctuation mean Fluctuation std Fluctuation skewness Onsets skewness Pulse clarity kurtosis Event density kurtosis Onsets mean
  76. 76. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  77. 77. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  78. 78. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  79. 79. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
  80. 80. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
  81. 81. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26% Window length: 1.4 seconds Algorithm utilised: Artificial Neural network
  82. 82. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  83. 83. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  84. 84. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  85. 85. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  86. 86. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  87. 87. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  88. 88. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  89. 89. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  90. 90. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  91. 91. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  92. 92. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  93. 93. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  94. 94. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence
  95. 95. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence
  96. 96. Thank you!

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